Related Experiment Video
Updated: Jun 22, 2025
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
15.6K
Detecting tipping points of complex diseases by network information entropy
Chengshang Lyu1,2, Lingxi Chen2, Xiaoping Liu1
1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Xiangshan Branch Alley, Xihu District, Hangzhou 310024, China.
Briefings in Bioinformatics
|July 3, 2024
Summary
This study introduces Network Information Entropy of Edges (NIEE), a new method to identify critical tipping points in complex disease progression. NIEE helps detect early disease stages and transformations for better understanding and intervention.
Area of Science:
- Complex Systems Biology
- Computational Biology
- Biomedical Data Analysis
Background:
- Complex disease progression is often non-linear, marked by critical transformations.
- Identifying these tipping points is vital for disease understanding and intervention.
Purpose of the Study:
- To develop a model-free method for detecting critical states in complex diseases.
- To enhance the understanding of disease development through early identification of tipping points.
Main Methods:
- Developed Network Information Entropy of Edges (NIEE), a model-free approach.
- Utilized dynamic network biomarkers, sample-specific networks, and information entropy.
- Applied NIEE to diverse data types, including bulk and single-sample expression data.
Main Results:
- Successfully identified critical predisease stages using NIEE.
- Detected tipping points preceding disease onset in real disease datasets.
- Demonstrated NIEE's capability across various data types.
Conclusions:
- NIEE is a powerful tool for detecting critical states in complex diseases.
- The method aids in understanding disease progression and identifying early intervention opportunities.
- Findings highlight NIEE's potential to advance complex disease research.
Related Concept Videos
02:26Protein Networks
3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
01:18Steps in Outbreak Investigation
122
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
122
01:25Statistical Methods for Analyzing Epidemiological Data
347
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
347
01:12Statistical Software for Data Analysis and Clinical Trials
532
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
532
01:24Kaplan-Meier Approach
123
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
123
01:11Hazard Rate
102
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
102